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» AdaBoost-Based Algorithm for Network Intrusion Detection
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CCS
2009
ACM
14 years 2 months ago
A framework for quantitative security analysis of machine learning
We propose a framework for quantitative security analysis of machine learning methods. Key issus of this framework are a formal specification of the deployed learning model and a...
Pavel Laskov, Marius Kloft
DATAMINE
2006
164views more  DATAMINE 2006»
13 years 7 months ago
Fast Distributed Outlier Detection in Mixed-Attribute Data Sets
Efficiently detecting outliers or anomalies is an important problem in many areas of science, medicine and information technology. Applications range from data cleaning to clinica...
Matthew Eric Otey, Amol Ghoting, Srinivasan Partha...
KDD
2008
ACM
195views Data Mining» more  KDD 2008»
14 years 7 months ago
Anomaly pattern detection in categorical datasets
We propose a new method for detecting patterns of anomalies in categorical datasets. We assume that anomalies are generated by some underlying process which affects only a particu...
Kaustav Das, Jeff G. Schneider, Daniel B. Neill
ECAI
2010
Springer
13 years 7 months ago
Mining Outliers with Adaptive Cutoff Update and Space Utilization (RACAS)
Recently the efficiency of an outlier detection algorithm ORCA was improved by RCS (Randomization with faster Cutoff update and Space utilization after pruning), which changes the ...
Chi-Cheong Szeto, Edward Hung
SIGCOMM
2010
ACM
13 years 7 months ago
NetShield: massive semantics-based vulnerability signature matching for high-speed networks
Accuracy and speed are the two most important metrics for Network Intrusion Detection/Prevention Systems (NIDS/NIPSes). Due to emerging polymorphic attacks and the fact that in ma...
Zhichun Li, Gao Xia, Hongyu Gao, Yi Tang, Yan Chen...